Metal Ion-Assisted Photochemical Vapor Generation for the Determination of Lead in Environmental Samples by Multicollector-ICPMS
Bibliographic record
Abstract
A novel and sensitive approach for the accurate and precise determination of Pb in environmental samples is presented using transition metal ion-assisted photochemical vapor generation (PVG) for sample introduction with multicollector inductively coupled plasma mass spectrometry (MC-ICPMS) detection. A significant improvement in PVG efficiency of lead is achieved in the presence of transition metal ions (Co(2+) and Ni(2+)) in solutions of 5% (v/v) formic acid. The determination of Pb in digests of sediment or soil samples is readily achieved due to coexisting transition metal ions which facilitate the PVG reaction. The method detection limit of 0.005 ng g(-1) (3σ) using external calibration is comparable to that obtained using hydride generation (HG) ICPMS. However, PVG methodology is simpler, results in lower blanks, and avoids unstable reagents. The accuracy of the proposed method was demonstrated by analysis of several environmental certified reference materials (CRMs; SLRS-5 and SRM1640a river water CRMs and MESS-3, MESS-4, and SRM2702 sediments) with satisfying results. High precision of determination (<0.4% RSD) of Pb in river water and sediments was realized on the basis of isotope dilution calibration.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".